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How Do Art Skills Influence Visual Search? – Eye Movements Analyzed With Hidden Markov Models
The results of two experiments are analyzed to find out how artistic expertise influences visual search. Experiment I comprised survey data of 1,065 students on self-reported visual memory skills and their ability to find three targets in four images of artwork. Experiment II comprised eye movement...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875865/ https://www.ncbi.nlm.nih.gov/pubmed/33584470 http://dx.doi.org/10.3389/fpsyg.2021.594248 |
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author | Tallon, Miles Greenlee, Mark W. Wagner, Ernst Rakoczy, Katrin Frick, Ulrich |
author_facet | Tallon, Miles Greenlee, Mark W. Wagner, Ernst Rakoczy, Katrin Frick, Ulrich |
author_sort | Tallon, Miles |
collection | PubMed |
description | The results of two experiments are analyzed to find out how artistic expertise influences visual search. Experiment I comprised survey data of 1,065 students on self-reported visual memory skills and their ability to find three targets in four images of artwork. Experiment II comprised eye movement data of 50 Visual Literacy (VL) experts and non-experts whose eye movements during visual search were analyzed for nine images of artwork as an external validation of the assessment tasks performed in Sample I. No time constraint was set for completion of the visual search task. A latent profile analysis revealed four typical solution patterns for the students in Sample I, including a mainstream group, a group that completes easy images fast and difficult images slowly, a fast and erroneous group, and a slow working student group, depending on task completion time and on the probability of finding all three targets. Eidetic memory, performance in art education and visual imagination as self-reported visual skills have significant impact on latent class membership probability. We present a hidden Markov model (HMM) approach to uncover underlying regions of attraction that result from visual search eye-movement behavior in Experiment II. VL experts and non-experts did not significantly differ in task time and number of targets found but they did differ in their visual search process: compared to non-experts, experts showed greater precision in fixating specific prime and target regions, assessed through hidden state fixation overlap. Exploratory analysis of HMMs revealed differences between experts and non-experts in image locations of attraction (HMM states). Experts seem to focus their attention on smaller image parts whereas non-experts used wider parts of the image during their search. Differences between experts and non-experts depend on the relative saliency of targets embedded in images. HMMs can determine the effect of expertise on exploratory eye movements executed during visual search tasks. Further research on HMMs and art expertise is required to confirm exploratory results. |
format | Online Article Text |
id | pubmed-7875865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78758652021-02-12 How Do Art Skills Influence Visual Search? – Eye Movements Analyzed With Hidden Markov Models Tallon, Miles Greenlee, Mark W. Wagner, Ernst Rakoczy, Katrin Frick, Ulrich Front Psychol Psychology The results of two experiments are analyzed to find out how artistic expertise influences visual search. Experiment I comprised survey data of 1,065 students on self-reported visual memory skills and their ability to find three targets in four images of artwork. Experiment II comprised eye movement data of 50 Visual Literacy (VL) experts and non-experts whose eye movements during visual search were analyzed for nine images of artwork as an external validation of the assessment tasks performed in Sample I. No time constraint was set for completion of the visual search task. A latent profile analysis revealed four typical solution patterns for the students in Sample I, including a mainstream group, a group that completes easy images fast and difficult images slowly, a fast and erroneous group, and a slow working student group, depending on task completion time and on the probability of finding all three targets. Eidetic memory, performance in art education and visual imagination as self-reported visual skills have significant impact on latent class membership probability. We present a hidden Markov model (HMM) approach to uncover underlying regions of attraction that result from visual search eye-movement behavior in Experiment II. VL experts and non-experts did not significantly differ in task time and number of targets found but they did differ in their visual search process: compared to non-experts, experts showed greater precision in fixating specific prime and target regions, assessed through hidden state fixation overlap. Exploratory analysis of HMMs revealed differences between experts and non-experts in image locations of attraction (HMM states). Experts seem to focus their attention on smaller image parts whereas non-experts used wider parts of the image during their search. Differences between experts and non-experts depend on the relative saliency of targets embedded in images. HMMs can determine the effect of expertise on exploratory eye movements executed during visual search tasks. Further research on HMMs and art expertise is required to confirm exploratory results. Frontiers Media S.A. 2021-01-28 /pmc/articles/PMC7875865/ /pubmed/33584470 http://dx.doi.org/10.3389/fpsyg.2021.594248 Text en Copyright © 2021 Tallon, Greenlee, Wagner, Rakoczy and Frick. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Tallon, Miles Greenlee, Mark W. Wagner, Ernst Rakoczy, Katrin Frick, Ulrich How Do Art Skills Influence Visual Search? – Eye Movements Analyzed With Hidden Markov Models |
title | How Do Art Skills Influence Visual Search? – Eye Movements Analyzed With Hidden Markov Models |
title_full | How Do Art Skills Influence Visual Search? – Eye Movements Analyzed With Hidden Markov Models |
title_fullStr | How Do Art Skills Influence Visual Search? – Eye Movements Analyzed With Hidden Markov Models |
title_full_unstemmed | How Do Art Skills Influence Visual Search? – Eye Movements Analyzed With Hidden Markov Models |
title_short | How Do Art Skills Influence Visual Search? – Eye Movements Analyzed With Hidden Markov Models |
title_sort | how do art skills influence visual search? – eye movements analyzed with hidden markov models |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875865/ https://www.ncbi.nlm.nih.gov/pubmed/33584470 http://dx.doi.org/10.3389/fpsyg.2021.594248 |
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